Budgeting utility costs can be difficult for several reasons. First, a utility provider may charge more per energy unit (e.g., per kilowatt hour) for electricity consumed when overall demand is high. For example, in desert areas, electricity may be twice as expensive during summer as during spring, due to an increased need to cool temperature down using weather conditioning systems. Second, a power consumer may not always benefit from inexpensive power supply even if it is available. For example, electricity produced by wind turbines, although inexpensive, cannot be fully taken advantage of without proper power equipments.
Moreover, under many tariff schedules, a both a load charge and an energy charge is levied. That is, the power consumer is charged for not only the amount of energy consumed, but also the rate at which it is consumed. Such a tariff schedule is designed to encourage constant rate power consumption. However, constant rate consumption is difficult to achieve in the face of a variable load demand. To add to the complexity, many tariff schedules charge separate load and energy charges for off-peak (standard), peak, and partial peak time intervals throughout the day. To further add to the complexity, many utility providers are moving towards real time pricing models which impose instantaneous load and energy charges based on real time energy production costs.
Thus, minimizing energy costs for the power consumer in the face of such complex tariff schedules represents a complex minimization problem, particularly when the power consumer can partially offset grid energy costs provided by the utility provider with off-grid renewable or partially renewable resources. The complex tariff schedules demand careful consideration of when and how such off-grid renewable or partially renewable resources are used to minimize the cost of power from the utility provider. In the face of such tariff schedules, the problem does not necessarily reduce to one in which the amount of power obtained from the power grid is minimized.
Given the above background, there is clearly a need in the art for systems and methods that can minimize costs incurred by grid power usage.